Deep learning interpretation of echocardiographic images predicts incident heart failure and subtypes

The authors aimed to determine whether deep learning analysis of echocardiographic images could improve the prediction of incident heart failure (HF) compared to existing clinical risk models. They developed and validated a model called Echo2HF using a large dataset, demonstrating that it accurately discriminated future HF risk with a 10-year AUROC of 0.83 and 0.82 in two independent cohorts, outperforming traditional risk scores. The findings suggest that integrating artificial intelligence in echocardiography may enhance HF prevention strategies and clinical outcomes.